The Timing of Transcriptional Regulation in Synthetic Gene Circuits

Abstract

Transcription factors and their target promoters are central to synthetic biology. By arranging these components into novel gene regulatory circuits, synthetic biologists have been able to create a wide variety of phenotypes, including bistable switches, oscillators, and logic gates. However, transcription factors (TFs) do not instantaneously regulate downstream targets. After the gene encoding a TF is turned on, the gene must first be transcribed, the transcripts must be translated, and sufficient TF must accumulate in order to bind operator sites of the target promoter. The time to complete this process, here called the “signaling time,” is a critical aspect in the design of dynamic regulatory networks, yet it remains poorly characterized. In this work, we measured the signaling time of two TFs in Escherichia coli commonly used in synthetic biology: the activator AraC and the repressor LacI. We found that signaling times can range from a few to tens of minutes, and are affected by the expression rate of the TF. Our single-cell data also show that the variability of the signaling time increases with its mean. To validate these signaling time measurements, we constructed a two-step genetic cascade, and showed that the signaling time of the full cascade can be predicted from those of its constituent steps. These results provide concrete estimates for the timescales of transcriptional regulation in living cells, which are important for understanding the dynamics of synthetic transcriptional gene circuits.

Distinct timescales of RNA regulators enable the construction of a genetic pulse generator

“…There have been a number of synthetic I1-FFLs built using protein regulators (Barone et al, 2017;Cheng, Hirning, Josić, & Bennett, 2017;Entus et al, 2007). Recently, we built a RNA-based I1-FFL that uses N-acyl homoserine lactone to activate expression of a STAR RNA that activates expression of monomeric red fluorescent protein (mRFP) as well as a gRNA and dCas9 that repress mRFP (Chappell et al, 2017).…”

Bayesian inference of distributed time delay in transcriptional and translational regulation

“…4a). To estimate this delay, we used a P BAD reporter-only circuit, 223 which we constructed previously by placing a YFP gene under control of the P BAD promoter in E. coli [41].…”

“…propensity function h k (y(t), θ k ), then sample θ k from the gamma posterior distribution. Otherwise, forms an incoherent feedforward loop and thus generates a single pulse of YFP [41]. Specifically, during the 150 decreasing phase after reaching the peak of the pulse, total YFP signals from a mother cell before a cell 151 division and from two daughter cells after the cell division were measured.…”

Dynamic variability in apoptotic threshold as a strategy for combating fractional killing

“…On the one hand, 2 some important cellular processes depend on precision in the timing of key intracellular 3 events, e.g., cell fate decision presumably requires a precise control of gene expression 4 timing [2][3][4][5][6][7][8][9][10][11][12][13][14][15], and the controls of cell cycle and circadian clocks require timing precision 5 that can be crucial for the correct physiology [16][17][18][19][20][21][22][23][24]. Most of these processes are based 6 on gene expression, e.g., an activated gene may be required to reach in a precise time threshold level of p53 to execute apoptosis and this threshold increases with time [26].…”

“…Most of these processes are based 6 on gene expression, e.g., an activated gene may be required to reach in a precise time threshold level of p53 to execute apoptosis and this threshold increases with time [26]. 13 Similarly, fractional killing of a cancer cell population by chemotherapy depends on 14 arrival time: the shorter the arrival time is, the more are the cells killed, indicating 15 the therapeutic efficacy is better. In short, timing precision and arrival time are two 16 important indices measuring the event timing.…”