The Growth Dependent Design Constraints of Transcription-Factor-Based Metabolite Biosensors

Abstract

Metabolite biosensors based on metabolite-responsive transcription factors are key synthetic biology components for sensing and precisely controlling cellular metabolism. Biosensors are often designed under laboratory conditions but are deployed in applications where cellular growth rate differs drastically from its initial characterization. Here we asked how growth rate impacts the minimum and maximum biosensor outputs and the dynamic range, which are key metrics of biosensor performance. Using LacI, TetR, and FadR-based biosensors in Escherichia coli as models, we find that the dynamic range of different biosensors have different growth rate dependencies. We developed a kinetic model to explore how tuning biosensor parameters impact the dynamic range growth rate dependence. Our modeling and experimental results revealed that the effects to dynamic range and its growth rate dependence are often coupled, and the metabolite transport mechanisms shape the dynamic range-growth rate response. This work provides a systematic understanding of biosensor performance under different growth rates, which will be useful for predicting biosensor behavior in broad synthetic biology and metabolic engineering applications.

Perspectives in growth production trade-off in microbial bioproduction

“…Metabolite responsive transcription factor based biosensors have been the most successful so far and hold immense potential but their dynamic range might be growth dependent and vary under different media conditions and growth environments. Recently it was reported that dynamic range of aTc-TetR and IPTG-LacI sensors have positive correlations with cell growth rate whereas FA-FadR biosensor has a negative correlation with cell growth rates when tested for several carbon sources in minimal media condition, confirming a tradeoff between dynamic range and growth condition 43 . Still, optimization is challenging when using a synthetic feed, as only narrow sweet spots exist.…”

“…Recently, it was reported that the dynamic range of aTc-TetR and IPTG-LacI sensors has positive correlations with the cell growth rate, whereas the FA-FadR biosensor has a negative correlation with the cell growth rates when tested for several carbon sources in minimal media condition, confirming the trade-off between the dynamic range and growth condition. 43 However, optimization is challenging when using a synthetic feed, given that only narrow sweet spots exist. These gene circuits are still not scalable across products and formats due to several challenges including their narrow dynamic range, linearity, and signal to noise ratio.…”

Applications and Tuning Strategies for Transcription Factor-Based Metabolite Biosensors

“…The most optimal TF expression level changes with the copy number of its operators in the sensor strain. The actual TF expression level also changes with cell growth conditions, which will need to be accounted for when using a sensor in different settings [ 43 ]. Increasing the copy number of TFs or reporter proteins can be used to create serial or parallel circuits, which have been shown capable of improving sensor sensitivity by 9-fold with minimal leaky expression and enhancing output signal intensity by 3.65-fold, respectively [ 44 ].…”

“…When the growth rate changes, such as when cells are switched from a well-controlled lab environment to industrially relevant conditions, the sensor’s dynamic range and signal levels may change drastically due to changes in the cell growth rate. Using three TF-promoter systems with repressed-repressor architectures, including TetR-P tet , LacI-P lacUV5 , and FadR-P AR , Hartline et al found that the dynamic ranges of the aTc and IPTG sensors increased with an increasing growth rate, while the dynamic range of the fatty acid (FA) sensor decreased with an increasing growth rate [ 43 ]. Further modeling work suggested that differences in growth rate dependence are caused by different metabolite transport mechanisms.…”

Revealing the chassis-effect on a broad-host-range genetic switch and its concordance with interspecies bacterial physiologies

“…Studies in the field of biosynthetic gene cluster expression 19 and microbial community engineering 20 have shown that similarities in phylogeny and genotypic profiles can accurately predict metabolic phenotype, which suggests genome relatedness could be a potential predictor of genetic circuit performance. On the other hand, the functional phenotype of expression plasmids and genetic devices has been shown to be coupled to physiological metrics such as growth rate 21, 22 , gene copy number 23, 24 , codon usage bias 25, 26 and growth burden 27, 28 in a number of studies. These studies have however, only considered a single or select combination of physiology metrics as explanatory variables of phenotype within a single model chassis.…”