A modular positive feedback-based gene amplifier

<p>Abstract</p> <p>Background</p> <p>Positive feedback is a common mechanism used in the regulation of many gene circuits as it can amplify the response to inducers and also generate binary outputs and hysteresis. In the context of electrical circuit design, positive feedback is often considered in the design of amplifiers. Similar approaches, therefore, may be used for the design of amplifiers in synthetic gene circuits with applications, for example, in cell-based sensors.</p> <p>Results</p> <p>We developed a modular positive feedback circuit that can function as a genetic signal amplifier, heightening the sensitivity to inducer signals as well as increasing maximum expression levels without the need for an external cofactor. The design utilizes a constitutively active, autoinducer-independent variant of the quorum-sensing regulator LuxR. We experimentally tested the ability of the positive feedback module to separately amplify the output of a one-component tetracycline sensor and a two-component aspartate sensor. In each case, the positive feedback module amplified the response to the respective inducers, both with regards to the dynamic range and sensitivity.</p> <p>Conclusions</p> <p>The advantage of our design is that the actual feedback mechanism depends only on a single gene and does not require any other modulation. Furthermore, this circuit can amplify any transcriptional signal, not just one encoded within the circuit or tuned by an external inducer. As our design is modular, it can potentially be used as a component in the design of more complex synthetic gene circuits.</p

A positive feedback-based gene circuit to increase the production of a membrane protein

<p>Abstract</p> <p>Background</p> <p>Membrane proteins are an important class of proteins, playing a key role in many biological processes, and are a promising target in pharmaceutical development. However, membrane proteins are often difficult to produce in large quantities for the purpose of crystallographic or biochemical analyses.</p> <p>Results</p> <p>In this paper, we demonstrate that synthetic gene circuits designed specifically to overexpress certain genes can be applied to manipulate the expression kinetics of a model membrane protein, cytochrome <it>bd </it>quinol oxidase in <it>E. coli</it>, resulting in increased expression rates. The synthetic circuit involved is an engineered, autoinducer-independent variant of the <it>lux </it>operon activator LuxR from <it>V. fischeri </it>in an autoregulatory, positive feedback configuration.</p> <p>Conclusions</p> <p>Our proof-of-concept experiments indicate a statistically significant increase in the rate of production of the <it>bd </it>oxidase membrane protein. Synthetic gene networks provide a feasible solution for the problem of membrane protein production.</p

Machine learning analysis of microbial flow cytometry data from nanoparticles, antibiotics and carbon sources perturbed anaerobic microbiomes

Abstract Background Flow cytometry, with its high throughput nature, combined with the ability to measure an increasing number of cell parameters at once can surpass the throughput of prevalent genomic and metagenomic approaches in the study of microbiomes. Novel computational approaches to analyze flow cytometry data will result in greater insights and actionability as compared to traditional tools used in the analysis of microbiomes. This paper is a demonstration of the fruitfulness of machine learning in analyzing microbial flow cytometry data generated in anaerobic microbiome perturbation experiments. Results Autoencoders were found to be powerful in detecting anomalies in flow cytometry data from nanoparticles and carbon sources perturbed anaerobic microbiomes but was marginal in predicting perturbations due to antibiotics. A comparison between different algorithms based on predictive capabilities suggested that gradient boosting (GB) and deep learning, i.e. feed forward artificial neural network with three hidden layers (DL) were marginally better under tested conditions at predicting overall community structure while distributed random forests (DRF) worked better for predicting the most important putative microbial group(s) in the anaerobic digesters viz. methanogens, and it can be optimized with better parameter tuning. Predictive classification patterns with DL (feed forward artificial neural network with three hidden layers) were found to be comparable to previously demonstrated multivariate analysis. The potential applications of this approach have been demonstrated for monitoring the syntrophic resilience of the anaerobic microbiomes perturbed by synthetic nanoparticles as well as antibiotics. Conclusion Machine learning can benefit the microbial flow cytometry research community by providing rapid screening and characterization tools to discover patterns in the dynamic response of microbiomes to several stimuli