Sprecher
Beschreibung
Many biochemical signal-processing pathways rely on families of proteins that competitively dimerize in diverse combinations. Such competitive dimerization networks (CDNs) enable complex input-output computations and context-specific adaptability by varying component expression levels. Inspired by this biological occurring paradigm and introducing the predictability and sequence specificity of DNA hybridization, we propose the development of a fully synthetic DNA-based dimerization network capable of sophisticated computational ability.
Our system employs DNA oligonucleotide monomers functionalized with reactive groups that covalently bond to form dimer outputs in an all-to-all or many-to-many fashion. Inputs can selectively bind and sequester specific monomers, preventing them from participating in the dimerization process and thus controlling the network’s outputs. This design enables highly programmable input-output computation, offering precise control over the synthesis of a selected dimer output. Furthermore, we demonstrate that the network’s size and complexity can be readily scaled, significantly expanding its computational capacity. Notably, DNA-based dimerization networks can regulate the yield of functional dimers outputs to drive downstream reactions, such as the controlled assembly or disassembly of multiple synthetic DNA nanostructures.
Building on these capabilities, we are also exploring strategies to integrate enzymatic control into these networks. By designing DNA components that respond to specific enzymatic actions, we can selectively activate or deactivate parts of the network, adding new layers of regulation and adaptability. Furthermore, by finely tuning the enzyme-driven reactions it is possible to modulate the availability of reactive sites in a temporal fashion. By incorporating such enzyme-responsive modules, we aim to create DNA-based dimerization networks whose behavior can be externally regulated by enzymatic cues, further expanding the versatility and programmability of these synthetic computational systems.
