A microfluidic optimal experimental design platform for forward design of cell-free genetic networks.

Cell-free protein synthesis has been widely used as a "breadboard" for design of synthetic genetic networks. However, due to a severe lack of modularity, forward engineering of genetic networks remains challenging. Here, we demonstrate how a combination of optimal experimental design and microfluidics allows us to devise dynamic cell-free gene expression experiments providing maximum information content for subsequent non-linear model identification. Importantly, we reveal that applying this methodology to a library of genetic circuits, that share common elements, further increases the information content of the data resulting in higher accuracy of model parameters. To show modularity of model parameters, we design a pulse decoder and bistable switch, and predict their behaviour both qualitatively and quantitatively. Finally, we update the parameter database and indicate that network topology affects parameter estimation accuracy. Utilizing our methodology provides us with more accurate model parameters, a necessity for forward engineering of complex genetic networks.

Transcription and Translation in Cytomimetic Protocells Perform Most Efficiently at Distinct Macromolecular Crowding Conditions.

The formation of cytomimetic protocells that capture the physicochemical aspects of living cells is an important goal in bottom-up synthetic biology. Here, we recreated the crowded cytoplasm in liposome-based protocells and studied the kinetics of cell-free gene expression in these crowded containers. We found that diffusion of key components is affected not only by macromolecular crowding but also by enzymatic activity in the protocell. Surprisingly, size-dependent diffusion in crowded conditions yielded two distinct maxima for protein synthesis, reflecting the differential impact of crowding on transcription and translation. Our experimental data show, for the first time, that macromolecular crowding induces a switch from reaction to diffusion control and that this switch depends on the sizes of the macromolecules involved. These results highlight the need to control the physical environment in the design of synthetic cells.

Protein Synthesis in Coupled and Uncoupled Cell-Free Prokaryotic Gene Expression Systems.

Secondary structure formation of mRNA, caused by desynchronization of transcription and translation, is known to impact gene expression in vivo. Yet, inactivation of mRNA by secondary structures in cell-free protein expression is frequently overlooked. Transcription and translation rates are often not highly synchronized in cell-free expression systems, leading to a temporal mismatch between the processes and a drop in efficiency of protein production. By devising a cell-free gene expression platform in which transcriptional and translational elongation are successfully performed independently, we determine that sequence-dependent mRNA secondary structures are the main cause of mRNA inactivation in in vitro gene expression.

Macromolecular crowding creates heterogeneous environments of gene expression in picolitre droplets.

Understanding the dynamics of complex enzymatic reactions in highly crowded small volumes is crucial for the development of synthetic minimal cells. Compartmentalized biochemical reactions in cell-sized containers exhibit a degree of randomness due to the small number of molecules involved. However, it is unknown how the physical environment contributes to the stochastic nature of multistep enzymatic processes. Here, we present a robust method to quantify gene expression noise in vitro using droplet microfluidics. We study the changes in stochasticity in the cell-free gene expression of two genes compartmentalized within droplets as a function of DNA copy number and macromolecular crowding. We find that decreased diffusion caused by a crowded environment leads to the spontaneous formation of heterogeneous microenvironments of mRNA as local production rates exceed the diffusion rates of macromolecules. This heterogeneity leads to a higher probability of the molecular machinery staying in the same microenvironment, directly increasing the system's stochasticity.