Editorial pubs.acs.org/synthbio
DNA 21 Collection his ACS Synthetic Biology collection contains papers based on research presented at the 21st International Conference on DNA Computing and Molecular Programming (DNA 21). The conference was held at the Wyss Institute for Biologically Inspired Engineering, at Harvard University, Massachusetts, USA, from August 17th to 21st, 2015, and organized under the auspices of the International Society for Nanoscale Science, Computation and Engineering (ISNSCE). Research in DNA computing and molecular programming draws together mathematics, computer science, physics, chemistry, biology, and nanotechnology to address the analysis, design, and synthesis of information-based molecular systems. The annual DNA conference is the premier forum where scientists with diverse backgrounds come together to present recent experimental and theoretical results in the field. Papers and presentations were sought in all areas that relate to biomolecular computing, including, but not restricted to algorithms and models for computation on biomolecular systems; computational processes in vitro and in vivo; molecular switches, gates, devices, and circuits; molecular folding and selfassembly of nanostructures; analysis and theoretical models of laboratory techniques; molecular motors and molecular robotics; studies of fault-tolerance and error correction; software tools for analysis, simulation, and design; synthetic biology and in vitro evolution; applications in engineering, physics, chemistry, biology, and medicine. Research in DNA Computing and Molecular Programming encompasses a broad range of theoretical and experimental work. It includes the development of architectures and modular components for programmable molecular systems, along with theoretical abstractions and software tools for describing and reasoning about these systems. It also involves the development of algorithms that operate effectively at the molecular scale, along with the identification of fundamental principles of what can be computed. Importantly, applications of this research to real-world problems are continually being explored, including in vitro diagnostics of pathogens, biomanufacturing of smart materials, high-precision methods for imaging and probing of biological experiments, and smart therapeutics. The papers in this collection encompass several aspects of this theoretical and practical research, and seek answers to questions that lie at the heart of the field. Lakin and Stefanovic present a framework for developing adaptive molecular circuits capable of long-term monitoring and control of biological systems. The framework involves the use of buffered DNA strand displacement networks, which contain reservoirs of inactive molecular complexes that are activated on-demand as the computation progresses. They apply this framework to the design and simulation of a DNA circuit for supervised learning of a class of linear functions, by stochastic gradient descent. The ability to perform long-running computations in DNA remains a challenging endeavor, since energy needs to be supplied to the system over an extended period. This work highlights buffered DNA strand displacement cascades as a promising architecture for implementing long-
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running computations solely using nucleic acids, and complements alternative strategies for long running computation that rely on additional transcription and translation machinery. Boemo et al. define a formal programming language and methodology for specifying an abstract propositional formula, and compiling this formula to a directed graph that captures the topology of a corresponding DNA circuit. Physical implementations are proposed as mappings to autonomous DNA walker circuits, where every propositional variable is represented by a track along an origami tile, together with a DNA walker. Methods for analyzing the circuit and optimizing its design are proposed, by mapping the circuit to a continuous time Markov chain. The localized architectures proposed in this paper require careful design and analysis to ensure that key mechanisms such as track blockage are robustly implemented. Localized computation using DNA is currently the subject of active theoretical and experimental research, and presents a number of potential advantages over systems consisting of wellmixed components, such as increased computation speed and reduced interference between components. Finally, Song et al. propose an architecture for the systematic construction of DNA circuits for analog computation, based on DNA strand displacement. Elementary analog gates for addition, subtraction, and multiplication are proposed, whose input and output values are directly encoded by concentrations of DNA species. Detailed domain designs and kinetic simulations of the gates are provided to demonstrate their expected performance. The paper also describes how analog DNA circuits to compute polynomial functions of inputs can be built using these gates. Analog computation is a promising approach to implementing efficient computation in synthetic biological systems, and this paper lays important groundwork for how such devices could be implemented in DNA. We express our sincere appreciation to the authors of the DNA 21 conference who presented their work at oral and poster sessions and submitted contributions to this collection. We also thank the reviewers and the ACS Synthetic Biology editors for the generous contribution of their time and technical expertise.
Andrew Phillips*
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Microsoft Research, Cambridge, UK
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Views expressed in this editorial are those of the author and not necessarily the views of the ACS.
Special Issue: DNA 21 Received: August 2, 2016 Published: August 19, 2016 877
DOI: 10.1021/acssynbio.6b00217 ACS Synth. Biol. 2016, 5, 877−877
