Towards spatial computing and chemical information storage in soft materials using DNA programming

Living organisms possess the ability to form and recover complex patterns in prescribed locations at length scales of hundreds of microns. During the past 15 years, experimentalists within the fields of DNA nanotechnology and synthetic biology have developed a variety of systems capable of self-assembly and reorganization at the nanoscale using synthetic oligonucleotide building blocks to mimic the functions of biological tissues and to provide new routes of manipulating materials with molecular programs. Programming ‘smart and responsive’ nano- and micromaterials using DNA circuits has the potential to impact numerous applications including molecular diagnostics, biodefense, drug delivery systems, and low-energy information storage. In this thesis, I present and develop computational and experimental systems that leverage oligonucleotide strand displacement reaction networks, digital maskless photolithographic technology, and microfluidic delivery methods to design DNA-functionalized micro-materials that process and store chemical information spatiotemporally. These systems couple reactions, transport, and feedback control to achieve specific temporal concentration profiles at specific points in hydrogel substrates. First, I developed a reaction-diffusion waveguide designed to coordinate spatiotemporal sensing and regulation of synthetic DNA- based materials using autocatalysis. I discuss the design requirements for this architecture and the results of in silico and experimental analyses of the components of this system. Based on the operational requirements of this system, I then designed a DNA-compatible hydrogel microfabrication method that accommodates UV photo-directed release of oligonucleotides from defined regions of a hydrogel, which can be used to initiate downstream reaction-diffusion processes in materials. Building on this platform, I constructed a reaction-diffusion system that enables shape programming of biomolecular attractor patterns in photopatterned poly(ethylene-glycol) diacrylate microgels. These patterns were able to heal their structure in response to spatial perturbation. Finally, I develop and discuss a model of a reaction-diffusion associative memory, consisting of a distributed network of nodes that store and repair spatial chemical patterns

Synthetic biology—putting engineering into biology

Synthetic biology is interpreted as the engineering-driven building of increasingly complex biological entities for novel applications. Encouraged by progress in the design of artificial gene networks, de novo DNA synthesis and protein engineering, we review the case for this emerging discipline. Key aspects of an engineering approach are purpose-orientation, deep insight into the underlying scientific principles, a hierarchy of abstraction including suitable interfaces between and within the levels of the hierarchy, standardization and the separation of design and fabrication. Synthetic biology investigates possibilities to implement these requirements into the process of engineering biological systems. This is illustrated on the DNA level by the implementation of engineering-inspired artificial operations such as toggle switching, oscillating or production of spatial patterns. On the protein level, the functionally self-contained domain structure of a number of proteins suggests possibilities for essentially Lego-like recombination which can be exploited for reprogramming DNA binding domain specificities or signaling pathways. Alternatively, computational design emerges to rationally reprogram enzyme function. Finally, the increasing facility of de novo DNA synthesis—synthetic biology’s system fabrication process—supplies the possibility to implement novel designs for ever more complex systems. Some of these elements have merged to realize the first tangible synthetic biology applications in the area of manufacturing of pharmaceutical compounds.

Kinetic Intersections as a Source of Information on Rate Coefficients

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Synthetic Biology: A Bridge between Artificial and Natural Cells.

Artificial cells are simple cell-like entities that possess certain properties of natural cells. In general, artificial cells are constructed using three parts: (1) biological membranes that serve as protective barriers, while allowing communication between the cells and the environment; (2) transcription and translation machinery that synthesize proteins based on genetic sequences; and (3) genetic modules that control the dynamics of the whole cell. Artificial cells are minimal and well-defined systems that can be more easily engineered and controlled when compared to natural cells. Artificial cells can be used as biomimetic systems to study and understand natural dynamics of cells with minimal interference from cellular complexity. However, there remain significant gaps between artificial and natural cells. How much information can we encode into artificial cells? What is the minimal number of factors that are necessary to achieve robust functioning of artificial cells? Can artificial cells communicate with their environments efficiently? Can artificial cells replicate, divide or even evolve? Here, we review synthetic biological methods that could shrink the gaps between artificial and natural cells. The closure of these gaps will lead to advancement in synthetic biology, cellular biology and biomedical applications

DNA as a universal substrate for chemical kinetics

Molecular programming aims to systematically engineer molecular and chemical systems of autonomous function and ever-increasing complexity. A key goal is to develop embedded control circuitry within a chemical system to direct molecular events. Here we show that systems of DNA molecules can be constructed that closely approximate the dynamic behavior of arbitrary systems of coupled chemical reactions. By using strand displacement reactions as a primitive, we construct reaction cascades with effectively unimolecular and bimolecular kinetics. Our construction allows individual reactions to be coupled in arbitrary ways such that reactants can participate in multiple reactions simultaneously, reproducing the desired dynamical properties. Thus arbitrary systems of chemical equations can be compiled into real chemical systems. We illustrate our method on the Lotka–Volterra oscillator, a limit-cycle oscillator, a chaotic system, and systems implementing feedback digital logic and algorithmic behavior