Workshop on DNA Computing by Self-Assembly

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Algorithmic self-assembly uses computational thinking to design systems in which large numbers of relatively simple components autonomously combine to form complex structures or perform computations. Applying tools from computational theory to this domain has resulted in the development of a rich set of theoretical models which have provided insight into many fundamental properties, abilities, and limitations of self-assembling systems. This has also helped to form a road map for experimental work in developing artificial self-assembling systems in the laboratory. During this workshop, several of those theoretical models will be introduced. The workshop presenter will then survey a wide variety of results in which notions of simulation between systems within, and even across, models have been used to compare and contrast the powers of those models and the systems they contain. The discussion will focus on the application of specific notions of simulation, including intrinsic universality, and the insights they lend into various properties of self-assembling systems. Intellectual MeritThe field of algorithmic self-assembly requires expertise in a broad range of areas, from computational theory to biochemistry, to name a few. This workshop will be designed to be accessible to those with no previous experience in self-assembly, and will start with introductions to basic definitions and models, but will finish with discussions of recent technical results which will also be of interest to experts in the field. Broader ImpactThe main goal of this workshop is to introduce a larger population of researchers, especially students, to the area of algorithmic self-assembly. Workshop organizers will use the funds provided to cover the travel costs of students attending the workshop co-located with Unconventional Computation and Natural Computation 2014 conference. The organizers will focus especially on funding students from underrepresented groups and with a variety of backgrounds to further broaden the exposure of algorithmic self-assembly. Further development of this field has the potential to lead to technological innovations which impact technologies from materials science, to biomedicine, to advanced computing architectures.