Engineering an efficient biomolecular circuit often requires time-consuming iterations of optimization. Cell-free protein expression systems allow rapid testing of biocircuits in vitro, speeding the design–build–test cycle of synthetic biology. In this paper, we combine this with droplet microfluidics to densely scan a transcription–translation biocircuit space. Our system assays millions of parameter combinations per hour, providing a detailed map of function. The ability to comprehensively map biocircuit parameter spaces allows accurate modeling to predict circuit function and identify optimal circuits and conditions.

Model-guided design has become a standard approach to engineering biomolecular circuits in synthetic biology. However, the stochastic nature of biomolecular reactions is often overlooked in the design process. As a result, cell–cell heterogeneity causes unexpected deviation of biocircuit behaviours from model predictions and requires additional iterations of design–build–test cycles. To enhance the design process of stochastic biocircuits, this paper presents a computational framework to systematically specify the level of intrinsic noise using well-defined metrics of statistics and design highly heterogeneous biocircuits based on the specifications. Specifically, we use descriptive statistics of population distributions as an intuitive specification language of stochastic biocircuits and develop an optimization-based computational tool that explores parameter configurations satisfying design requirements. Sensitivity analysis methods are also performed to ensure the robustness of a biocircuit design against extrinsic perturbations. These design tools are formulated with convex optimization programs to enable rigorous and efficient quantification of the statistics. We demonstrate these features by designing a stochastic negative feedback biocircuit that satisfies multiple statistical constraints and perform an in-depth study of noise propagation and regulation in negative feedback pathways.

The predictive ability of stochastic chemical reactions is currently limited by the lack of closed form solutions to the governing chemical master equation. To overcome this limitation, this letter proposes a computational method capable of predicting mathematically rigorous upper and lower bounds of transient moments for reactions governed by the law of mass action. We first derive an equation that transient moments must satisfy based on the moment equation. Although this equation is underdetermined, we introduce a set of semidefinite constraints known as moment condition to narrow the feasible set of the variables in the equation. Using these conditions, we formulate a semidefinite program that efficiently and rigorously computes the bounds of transient moment dynamics. The proposed method is demonstrated with illustrative numerical examples and is compared with related works to discuss advantages and limitations.

The correlation between gas concentrations in human breath and diseases has been increasingly revealed in recent years, triggering the need for cheap and easy-to-use gas sensors that can detect diseases in their early stages. The gas sensors used in these clinical applications need to be portable and sensitive, and preferably provide on-demand measurements for prompt diagnosis. In this paper, we propose a portable and cost-effective sensor platform that can quickly identify gas concentrations in human breath. Specifically, we combined quartz crystal microbalance (QCM) sensors with a single board computer, Raspberry Pi, to enable real-time data processing and display of the sensor data. A web server was operated on the single board computer, and thus, on-demand visualization of the sensor data was possible using a web browser. A gas quantification protocol was also proposed based on Freundlich's adsorption isotherm. We demonstrated the real-time monitoring of methyl mercaptan (MM) gas over the internet using the developed small sensor device, which can easily be handled in one hand. The limit of detection (LOD) for MM gas was 107 ppb when the sensors were operated for 20 minutes after gas injection. The developed gas sensing platform is unmatched in terms of size and cost compared with analytical devices though less sensitive. These advantages would allow for wide distribution of the developed device to hospitals and individuals in large quantities, leading for behavioral changes for preventive care.