In situ synthesis and dynamic simulation of molecularly imprinted polymeric nanoparticles on a micro-reactor system.

Current practices in synthesizing molecularly imprinted polymers face challenges-lengthy process, low-productivity, the need for expensive and sophisticated equipment, and they cannot be controlled in situ synthesis. Herein, we present a micro-reactor for in situ and continuously synthesizing trillions of molecularly imprinted polymeric nanoparticles that contain molecular fingerprints of bovine serum albumin in a short period of time (5-30 min). Initially, we performed COMSOL simulation to analyze mixing efficiency with altering flow rates, and experimentally validated the platform for synthesizing nanoparticles with sizes ranging from 52-106 nm. Molecular interactions between monomers and protein were also examined by molecular docking and dynamics simulations. Afterwards, we benchmarked the micro-reactor parameters through dispersity and concentration of molecularly imprinted polymers using principal component analysis. Sensing assets of molecularly imprinted polymers were examined on a metamaterial sensor, resulting in 81% of precision with high selectivity (4.5 times), and three cycles of consecutive use. Overall, our micro-reactor stood out for its high productivity (48-288 times improvement in assay-time and 2 times improvement in reagent volume), enabling to produce 1.4-1.5 times more MIPs at one-single step, and continuous production compared to conventional strategy.

SARS-CoV-2 Detection with De Novo-Designed Synthetic Riboregulators.

SARS-CoV-2 is a human pathogen and the main cause of COVID-19 disease, announced as a global pandemic by the World Health Organization. COVID-19 is characterized by severe conditions, and early diagnosis can make dramatic changes for both personal and public health. Low-cost, easy-to-use diagnostic capabilities can have a very critical role in controlling the transmission of the disease. Here, we are reporting a state-of-the-art diagnostic tool developed with an in vitro synthetic biology approach by employing engineered de novo riboregulators. Our design coupled with a home-made point-of-care device can detect and report the presence of SARS-CoV-2-specific genes. The presence of SARS-CoV-2-related genes triggers the translation of sfGFP mRNAs, resulting in a green fluorescence output. The approach proposed here has the potential of being a game changer in SARS-CoV-2 diagnostics by providing an easy-to-run, low-cost diagnostic capability.

Genetic circuits combined with machine learning provides fast responding living sensors.

Whole cell biosensors (WCBs) have become prominent in many fields from environmental analysis to biomedical diagnostics thanks to advanced genetic circuit design principles. Despite increasing demand on cost effective and easy-to-use assessment methods, a considerable amount of WCBs retains certain drawbacks such as long response time, low precision and accuracy. Here, we utilized a neural network-based architecture to improve the features of WCBs and engineered a gold sensing WCB which has a long response time (18 h). Two Long-Short Term-Memory (LSTM)-based networks were integrated to assess both ON/OFF and concentration dependent states of the sensor output, respectively. We demonstrated that binary (ON/OFF) network was able to distinguish between ON/OFF states as early as 30 min with 78% accuracy and over 98% in 3 h. Furthermore, when analyzed in analog manner, we demonstrated that network can classify the raw fluorescence data into pre-defined analyte concentration groups with high precision (82%) in 3 h. This approach can be applied to a wide range of WCBs and improve rapidness, simplicity and accuracy which are the main challenges in synthetic biology enabled biosensing.