scholarly journals The Effect of Compositional Context on Synthetic Gene Networks

2016 ◽  
Author(s):  
Enoch Yeung ◽  
Aaron J. Dy ◽  
Kyle B. Martin ◽  
Andrew H. Ng ◽  
Domitilla Del Vecchio ◽  
...  

SUMMARYIt is well known that synthetic gene expression is highly sensitive to how comprising genetic elements (promoter structure, spacing regions between promoter and coding sequences, ribosome binding sites, etc.) are spatially configured. An important topic that has received far less attention is how the physical layout of entire genes within a synthetic gene network affects their individual expression levels. In this paper we show, both quantitatively and qualitatively, that compositional context can significantly alter expression levels in synthetic gene networks. We also show that these compositional context effects are pervasive both at the transcriptional and translational level. Further, we demonstrate that key characteristics of gene induction, such as ultra-sensitivity and dynamic range, are heavily dependent on compositional context. We postulate that supercoiling can be used to explain these interference effects and validate this hypothesis through modeling and a series of in vitro supercoiling relaxation experiments. On the whole, these results suggest that compositional context introduces feedback in synthetic gene networks. As an illustrative example, we show that a design strategy incorporating compositional context effects can improve threshold detection and memory properties of the toggle switch.

2021 ◽  
Vol 18 (182) ◽  
pp. 20210413
Author(s):  
Enoch Yeung ◽  
Jongmin Kim ◽  
Ye Yuan ◽  
Jorge Gonçalves ◽  
Richard M. Murray

Synthetic gene networks are frequently conceptualized and visualized as static graphs. This view of biological programming stands in stark contrast to the transient nature of biomolecular interaction, which is frequently enacted by labile molecules that are often unmeasured. Thus, the network topology and dynamics of synthetic gene networks can be difficult to verify in vivo or in vitro , due to the presence of unmeasured biological states. Here we introduce the dynamical structure function as a new mesoscopic, data-driven class of models to describe gene networks with incomplete measurements of state dynamics. We develop a network reconstruction algorithm and a code base for reconstructing the dynamical structure function from data, to enable discovery and visualization of graphical relationships in a genetic circuit diagram as time-dependent functions rather than static, unknown weights. We prove a theorem, showing that dynamical structure functions can provide a data-driven estimate of the size of crosstalk fluctuations from an idealized model. We illustrate this idea with numerical examples. Finally, we show how data-driven estimation of dynamical structure functions can explain failure modes in two experimentally implemented genetic circuits, a previously reported in vitro genetic circuit and a new E. coli -based transcriptional event detector.


2010 ◽  
Vol 21 (5) ◽  
pp. 690-696 ◽  
Author(s):  
Wilfried Weber ◽  
Martin Fussenegger

2012 ◽  
Vol 23 (5) ◽  
pp. 703-711 ◽  
Author(s):  
Maria Karlsson ◽  
Wilfried Weber

iScience ◽  
2019 ◽  
Vol 14 ◽  
pp. 323-334 ◽  
Author(s):  
Alex J.H. Fedorec ◽  
Tanel Ozdemir ◽  
Anjali Doshi ◽  
Yan-Kay Ho ◽  
Luca Rosa ◽  
...  

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