Extrinsic Noise Effects Regulation at the Single Gene and Small Gene Network Levels
Mohamed Bahrudeen, Mohamed Nasurudeen (2017)
Mohamed Bahrudeen, Mohamed Nasurudeen
2017
Electrical Engineering
Tieto- ja sähkötekniikan tiedekunta - Faculty of Computing and Electrical Engineering
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Hyväksymispäivämäärä
2017-11-08
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tty-201710132008
https://urn.fi/URN:NBN:fi:tty-201710132008
Tiivistelmä
Recent studies of gene expression in Escherichia coli using novel in vivo measurement techniques revealed that protein and RNA numbers from a gene differ between genetically identical cells. To unravel the causes for this, measurements were conducted and models were developed. These studies revealed that this diversity arises from extrinsic and intrinsic noise. The former is due to cell-to-cell variability in numbers of molecules involved, such as RNA polymerase (RNAp), transcription factors, etc. The latter is due to the stochastic nature of the chemical reactions combined with the fact that the molecules and genes involved exist in small numbers.
One aspect that has not been given much attention so far, is the unique nature of the dynamics of transcription of each promoter of the gene regulatory network (GRN). This process has multiple rate-limiting steps whose duration differs between promoters. How this may diversify the variability in RNA and protein numbers between genes is unknown.
To address this, we use single-cell empirical data and stochastic models with empirically validated parameter values and study how the kinetics of transcription of a gene affects the influence of extrinsic noise on the kinetics. Interestingly, we find that promoters whose open complex formation is longer lasting tend to suppress the propagation of extrinsic noise that affects only the steps prior to initiation of the open complex formation.
In particular, our studies indicate that the cell-to-cell variability in RNA numbers depends on the transcription kinetics. As such, it is sequence-dependent. Further, in a 2-gene toggle switch, we find that its mean switching frequency depends on the transcription kinetics of the promoters but not on the cell-to-cell RNAp variability. On the other hand, the cell-to-cell variability in switching frequency is affected by these two variables. Meanwhile, in a Repressilator network (3 genes where each gene represses the next), we measured the mean and standard deviation of the period of oscillation. From these measurements in silico, we found that both parameters are independent of the RNAP cell-to-cell variability, but are strongly controlled by the transcription kinetics of each of its genes.
We conclude that the transcription kinetics of the component genes is a key regulator of small genetic circuits, as it can be used as a tunable filter of extrinsic noise. Overall, the kinetics of the rate-limiting steps in transcription of individual genes act as ‘master regulators’ of the expression of individual genes and the behavior of genetic cir-cuits’, such as switching dynamics, period of oscillation, etc.
One aspect that has not been given much attention so far, is the unique nature of the dynamics of transcription of each promoter of the gene regulatory network (GRN). This process has multiple rate-limiting steps whose duration differs between promoters. How this may diversify the variability in RNA and protein numbers between genes is unknown.
To address this, we use single-cell empirical data and stochastic models with empirically validated parameter values and study how the kinetics of transcription of a gene affects the influence of extrinsic noise on the kinetics. Interestingly, we find that promoters whose open complex formation is longer lasting tend to suppress the propagation of extrinsic noise that affects only the steps prior to initiation of the open complex formation.
In particular, our studies indicate that the cell-to-cell variability in RNA numbers depends on the transcription kinetics. As such, it is sequence-dependent. Further, in a 2-gene toggle switch, we find that its mean switching frequency depends on the transcription kinetics of the promoters but not on the cell-to-cell RNAp variability. On the other hand, the cell-to-cell variability in switching frequency is affected by these two variables. Meanwhile, in a Repressilator network (3 genes where each gene represses the next), we measured the mean and standard deviation of the period of oscillation. From these measurements in silico, we found that both parameters are independent of the RNAP cell-to-cell variability, but are strongly controlled by the transcription kinetics of each of its genes.
We conclude that the transcription kinetics of the component genes is a key regulator of small genetic circuits, as it can be used as a tunable filter of extrinsic noise. Overall, the kinetics of the rate-limiting steps in transcription of individual genes act as ‘master regulators’ of the expression of individual genes and the behavior of genetic cir-cuits’, such as switching dynamics, period of oscillation, etc.