Two cellular resource‐based models linking growth and parts characteristics aids the study and optimisation of synthetic gene circuits

AbstractGene expression control based on CRISPRi (clustered regularly interspaced short palindromic repeats interference) has emerged as a powerful tool for creating synthetic gene circuits, both in prokaryotes and in eukaryotes; yet, its lack of cooperativity has been pointed out as a potential obstacle for dynamic or multistable circuit construction. Here we use CRISPRi to build prominent synthetic gene circuits in Escherichia coli. We report the first-ever CRISPRi oscillator (“CRISPRlator”), bistable network (toggle switch) and stripe pattern-forming incoherent feed-forward loop (IFFL). Our circuit designs, conceived to feature high predictability and orthogonality, as well as low metabolic burden and context-dependency, allowed us to achieve robust circuit behaviors. Mathematical modeling suggests that unspecific binding in CRISPRi is essential to establish multistability. Our work demonstrates the wide applicability of CRISPRi in synthetic circuits and paves the way for future efforts towards engineering more complex synthetic networks, boosted by the advantages of CRISPR technology.

Download Full-text

Synthetic Gene Circuits for Antimicrobial Resistance and Cancer Research

10.5772/intechopen.99329 ◽

2021 ◽

Author(s):

Kevin S. Farquhar ◽

Michael Tyler Guinn ◽

Gábor Balázsi ◽

Daniel A. Charlebois

Keyword(s):

Mammalian Cells ◽

Rational Design ◽

Study Drug ◽

Quantitative Model ◽

Synthetic Gene ◽

Modeling Tools ◽

Gene Circuits ◽

Novel Treatments ◽

Synthetic Gene Circuits ◽

Temporal Dimensions

Mathematical models and synthetic gene circuits are powerful tools to develop novel treatments for patients with drug-resistant infections and cancers. Mathematical modeling guides the rational design of synthetic gene circuits. These systems are then assembled into unified constructs from existing and/or modified genetic components from a range of organisms. In this chapter, we describe modeling tools for the design and characterization of chemical- and light-inducible synthetic gene circuits in different organisms and highlight how synthetic gene circuits are advancing biomedical research. Specifically, we demonstrate how these quantitative model systems are being used to study drug resistance in microbes and to probe the spatial–temporal dimensions of cancer in mammalian cells.

Download Full-text

Characterizing microRNA-mediated modulation of gene expression noise and its effect on synthetic gene circuits

10.1101/2020.07.08.193094 ◽

2020 ◽

Author(s):

Lei Wei ◽

Shuailin Li ◽

Tao Hu ◽

Michael Q. Zhang ◽

Zhen Xie ◽

...

Keyword(s):

Gene Expression ◽

Cell Types ◽

Classification Performance ◽

Synthetic Gene ◽

Gene Circuits ◽

Gene Expression Noise ◽

Expression Noise ◽

Synthetic Gene Circuits ◽

Highly Expressed Genes ◽

Lower Noise

AbstractGene expression noise plays an important role in many biological processes, such as cell differentiation and reprogramming. It can also dramatically influence the behavior of synthetic gene circuits. MicroRNAs (miRNAs) have been shown to reduce the noise of lowly expressed genes and increase the noise of highly expressed genes, but less is known about how miRNAs with different properties may regulate gene expression noise differently. Here, by quantifying gene expression noise using mathematical modeling and experimental measurements, we showed that competing RNAs and the composition of miRNA response elements (MREs) play important roles in modulating gene expression noise. We found that genes targeted by miRNAs with weak competing RNAs show lower noise than those targeted by miRNAs with strong competing RNAs. In addition, in comparison with a single MRE, repetitive MREs targeted by the same miRNA suppress the noise of lowly expressed genes but increase the noise of highly expressed genes. Additionally, MREs composed of different miRNA targets could cause similar repression levels but lower noise compared with repetitive MREs. We further observed the influence of miRNA-mediated noise modulation in synthetic gene circuits which could be applied to classify cell types using miRNAs as sensors. We found that miRNA sensors that introduce higher noise could lead to better classification performance. Our results provide a systematic and quantitative understanding of the function of miRNAs in controlling gene expression noise and how we can utilize miRNAs to modulate the behavior of synthetic gene circuits.

Download Full-text

Winner-Takes-All Resource Competition Redirects Cascading Cell Fate Transitions

10.1101/2020.05.23.103259 ◽

2020 ◽

Cited By ~ 1

Author(s):

Rong Zhang ◽

Hanah Goetz ◽

Juan Melendez-Alvarez ◽

Jiao Li ◽

Tian Ding ◽

...

Keyword(s):

Cell Fate ◽

Resource Competition ◽

Synthetic Gene ◽

Transition Path ◽

Gene Circuits ◽

Single Strain ◽

Significant Challenge ◽

Synthetic Gene Circuits ◽

Winner Takes All

AbstractFailure of modularity remains a significant challenge for synthetic gene circuits assembled with tested modules as they often do not function as expected. Competition over shared limited gene expression resources is a crucial underlying reason. Here, we first built a synthetic cascading bistable switches (Syn-CBS) circuit in a single strain with two coupled self-activation modules to achieve two successive cell fate transitions. Interestingly, we found that the in vivo transition path was redirected as the activation of one switch always prevailed against the other instead of the theoretically expected coactivation. This qualitatively different type of resource competition between the two modules follows a ‘winner-takes-all’ rule, where the winner is determined by the relative connection strength between the modules. To decouple the resource competition, we constructed a two-strain circuit, which achieved successive activation and stable coactivation of the two switches. We unveiled a nonlinear resource competition within synthetic gene circuits and provided a division of labor strategy to minimize unfavorable effects.

Download Full-text