Dimethyl sulfoxide (DMSO) has been broadly used in biology as a cosolvent, a cryoprotectant, and an enhancer of membrane permeability, leading to the general assumption that DMSO-induced structural changes in cell membranes and their hydration water play important functional roles. Although the effects of DMSO on the membrane structure and the headgroup dehydration have been extensively studied, the mechanism by which DMSO invokes its effect on lipid membranes and the direct role of water in this process are unresolved. By directly probing the translational water diffusivity near unconfined lipid vesicle surfaces, the lipid headgroup mobility, and the repeat distances in multilamellar vesicles, we found that DMSO exclusively weakens the surface water network near the lipid membrane at a bulk DMSO mole fraction (XDMSO) of <0.1, regardless of the lipid composition and the lipid phase. Specifically, DMSO was found to effectively destabilize the hydration water structure at the lipid membrane surface at XDMSO <0.1, lower the energetic barrier to dehydrate this surface water, whose displacement otherwise requires a higher activation energy, consequently yielding compressed interbilayer distances in multilamellar vesicles at equilibrium with unaltered bilayer thicknesses. At XDMSO >0.1, DMSO enters the lipid interface and restricts the lipid headgroup motion. We postulate that DMSO acts as an efficient cryoprotectant even at low concentrations by exclusively disrupting the water network near the lipid membrane surface, weakening the cohesion between water and adhesion of water to the lipid headgroups, and so mitigating the stress induced by the volume change of water during freeze-thaw.

Understanding the dynamics of complex enzymatic reactions in highly crowded small volumes is crucial for the development of synthetic minimal cells. Compartmentalised 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 cellfree gene expression of two genes compartmentalised 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 micro-environments of mRNA as local production rates exceed diffusion rates of macromolecules. This heterogeneity leads to a higher probability of the molecular machinery to stay in the same microenvironment, directly increasing the system's stochasticity.Noise is present in all living cells. It has been studied in prokaryotes and eukaryotes 1 , as well as stem 2,3 and cancer cells 4 , and cells expressing viruses 5 . Gene expression is a key example of a complex stochastic enzymatic process. Careful analysis of variations in mRNA and protein levels has revealed the importance of both amplitude and typical decay time of noise and the ability of cells to exploit or suppress noise in gene expression [6][7][8][9] . Unlike deterministic models of gene expression, which are used to predict dynamics over large populations, stochastic models can correctly predict the dynamics of gene expression at the Reprints and permission information is available online at http://npg.nature.com/reprintsandpermissions/.Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use

DNA-based molecular circuits allow autonomous signal processing, but their actuation has relied mostly on RNA/DNA-based inputs, limiting their application in synthetic biology, biomedicine and molecular diagnostics. Here we introduce a generic method to translate the presence of an antibody into a unique DNA strand, enabling the use of antibodies as specific inputs for DNA-based molecular computing. Our approach, antibody-templated strand exchange (ATSE), uses the characteristic bivalent architecture of antibodies to promote DNA-strand exchange reactions both thermodynamically and kinetically. Detailed characterization of the ATSE reaction allowed the establishment of a comprehensive model that describes the kinetics and thermodynamics of ATSE as a function of toehold length, antibody–epitope affinity and concentration. ATSE enables the introduction of complex signal processing in antibody-based diagnostics, as demonstrated here by constructing molecular circuits for multiplex antibody detection, integration of multiple antibody inputs using logic gates and actuation of enzymes and DNAzymes for signal amplification.

Living cells are able to produce a wide variety of biological responses when subjected to biochemical stimuli. It has become apparent that these biological responses are regulated by complex chemical reaction networks (CRNs). Unravelling the function of these circuits is a key topic of both systems biology and synthetic biology. Recent progress at the interface of chemistry and biology together with the realisation that current experimental tools are insufficient to quantitatively understand the molecular logic of pathways inside living cells has triggered renewed interest in the bottom-up development of CRNs. This builds upon earlier work of physical chemists who extensively studied inorganic CRNs and showed how a system of chemical reactions can give rise to complex spatiotemporal responses such as oscillations and pattern formation. Using purified biochemical components, in vitro synthetic biologists have started to engineer simplified model systems with the goal of mimicking biological responses of intracellular circuits. Emulation and reconstruction of system-level properties of intracellular networks using simplified circuits are able to reveal key design principles and molecular programs that underlie the biological function of interest. In this Tutorial Review, we present an accessible overview of this emerging field starting with key studies on inorganic CRNs followed by a discussion of recent work involving purified biochemical components. Finally, we review recent work showing the versatility of programmable biochemical reaction networks (BRNs) in analytical and diagnostic applications.

Inspired by signaling networks in living cells, DNA-based programming aims for the engineering of biochemical networks capable of advanced regulatory and computational functions under controlled cell-free conditions. While regulatory circuits in cells control downstream processes through hierarchical layers of signal processing, coupling of enzymatically driven DNA-based networks to downstream processes has rarely been reported. Here, we expand the scope of molecular programming by engineering hierarchical control of enzymatic actuators using feedback-controlled DNA-circuits capable of advanced regulatory dynamics. We developed a translator module that converts signaling molecules from the upstream network to unique DNA strands driving downstream actuators with minimal retroactivity and support these findings with a detailed computational analysis. We show our modular approach by coupling of a previously engineered switchable memories circuit to downstream actuators based on β-lactamase and luciferase. To the best of our knowledge, our work demonstrates one of the most advanced DNA-based circuits regarding complexity and versatility.