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Functional analysis of artificial DNA reaction network

Abstract : Information processing within and in between living organisms involves the production and exchange of molecules through signaling pathways organized in chemical reactions networks. They are various by their shape, size, and by the nature of the molecules embroiled. Among them, gene regulatory networks were our inspiration to develop and implement a new framework for in-vitro molecular programming. Indeed, the expression of a gene is mostly controlled by transcription factors or regulatory proteins and/or nucleic acids that are themselves triggered by other genes. The whole assembly draws a web of cross-interacting genes and their subproducts, in which the well controlled topology relates to a precise function. With a closer look at the links between nodes in such architectures, we identify three key points in the inner operating system. First, the interactions either activate or inhibit the production of the later node, meaning that non trivial behaviors are obtained by a combination of nodes rather than a specific new interaction. Second, the chemical stability of DNA, together with the precise reactivity of enzymes ensures the longevity of the network. Finally, the dynamics are sustained by the constant anabolism/catabolism of the effectors, and the subsequent use of fuel/energy. All together, these observations led us to develop an original set of 3 elementary enzymatic reactions: the PEN-DNA toolbox. The architecture of the assembly, i.e. the connectivity between nodes relies on the sequence of synthetic DNA strands (called DNA templates), and 3 enzymes (a polymerase, a nickase and an exonuclease) are taking care of catalysis. The production and degradation of intermediates consume deoxyribonucleoside triphosphates (dNTP) and produce deoxynucleotide monophosphates leading to the dissipation of chemical potential. Reactions are monitored thanks to a backbone modification of a template with a fluorophore and the nucleobase quenching effect consecutive to an input strand binding the template. The activation mechanism is then the production of an output following the triggering of an input strand, and the inhibition comes from the production of an output strand that binds the activator-producing sequence. Various behaviors such as oscillation, bistability, or switchable memory have been implemented, requiring more and more complex topologies. For that, each circuit requires a fine tuning in the amount of chemical parameters, such as templates and enzymes. This underlies the fact that a given network may lead to different demeanors depending on the set of parameters. Mapping the output of each combination in the parameter space to find out the panel of behaviors leads to the bifurcation diagram of the system. In order to explore exhaustively the possibilities of one circuit with a reasonable experimental cost, we developped a microfluidic tool generating picoliter-sized water-in-oil droplets with different contents. We overcame the technical challenges in hardware (microfluidic design, droplet generation and long-term observation) and wetware (tracability of the droplet and emulsion compatibility/stability). So far, bifurcation diagrams were calculated from mathematical models based on the enzymes kinetics and the thermodynamic properties of each reaction. The model was then fitted with experimental data taken in distant points in the parameter space. Here, millions of droplets are created, and each one encloses a given amount of parameters, becoming one point in the diagram. The parameter coordinates are barcoded in the droplet, and the output fluorescence signal is recorded by time lapse microscopy. We first applied this technique to a well-known network, and obtained the first experimental two-dimensional bifurcation diagram of the bistable system. The diagram enlightens features that were not described by the previous mathematical model. (...)
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Submitted on : Monday, August 28, 2017 - 4:40:54 PM
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  • HAL Id : tel-01578138, version 1



Alexandre Baccouche. Functional analysis of artificial DNA reaction network. Organic chemistry. Université Sorbonne Paris Cité, 2015. English. ⟨NNT : 2015USPCB135⟩. ⟨tel-01578138⟩



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