Modeling Scalable Pattern Generation in DNA Reaction Networks.

We have developed a theoretical framework for developing patterns in multiple dimensions using controllable diffusion and designed reactions implemented in DNA. This includes so-called strand displacement reactions in which one single-stranded DNA hybridizes to a hemi-duplex DNA and displaces another single-stranded DNA, reversibly or irreversibly. These reactions can be designed to proceed with designed rate and molecular specificity. By also controlling diffusion by partial complementarity to a stationary, cross-linked DNA, we can generate predictable patterns. We demonstrate this with several simulations showing deterministic, predictable shapes in space.

Efficient molecular encoding in multifunctional self-immolative urethanes.

Molecular encoding in sequence-defined polymers shows promise as a new paradigm for data storage. Here, we report what is, to our knowledge, the first use of self-immolative oligourethanes for storing and reading encoded information. As a proof of principle, we describe how a text passage from Jane Austen's Mansfield Park was encoded in sequence-defined oligourethanes and reconstructed via self-immolative sequencing. We develop Mol.E-coder, a software tool that uses a Huffman encoding scheme to convert the character table to hexadecimal. The oligourethanes are then generated by a high-throughput parallel synthesis. Sequencing of the oligourethanes by self-immolation is done concurrently in a parallel fashion, and the liquid chromatography-mass spectrometry (LC-MS) information decoded by our Mol.E-decoder software. The passage is capable of being reproduced wholly intact by a third-party, without any purifications or the use of tandem MS (MS/MS), despite multiple rounds of compression, encoding, and synthesis.

Pattern transformation with DNA circuits.

Readily programmable chemical networks are important tools as the scope of chemistry expands from individual molecules to larger molecular systems. Although many complex systems are constructed using conventional organic and inorganic chemistry, the programmability of biological molecules such as nucleic acids allows for precise, high-throughput and automated design, as well as simple, rapid and robust implementation. Here we show that systematic and quantitative control over the diffusivity and reactivity of DNA molecules yields highly programmable chemical reaction networks (CRNs) that execute at the macroscale. In particular, we designed and implemented non-enzymatic DNA circuits capable of performing pattern-transformation algorithms such as edge detection. We also showed that it is possible to fine-tune and multiplex such circuits. We believe these strategies will provide programmable platforms on which to prototype CRNs, discover bottom-up construction principles and generate patterns in materials.