Frontiers of Engineering Reports on Leading-Edge Engineering from the 2003 NAE Symposium on Frontiers of Engineering (2004) / Chapter Skim
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Biomolecular Computing
Biomolecular Computing
Pages 103-130
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using the astounding parallelism of chemistry to solve mathematical problems, such as combinatorial search problems; and (2) using biochemical algorithms to direct and control molecular processes, such as complex fabrication tasks.
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DNA Computing Leonard Adleman's original paper on DNA computing contained the seed of the idea we'll pursue here that the programmability of DNA hybridization reactions can be used to direct self-assembly according to simple rules. In the first combinatorial-generation step of Adleman's procedure, DNA molecules representing all possible paths through the target graph were assembled by DNA hybridization in a single step.
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107 lo l O
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precise rules for crystal growth that can be implemented reliably. DNA Nanotechnology We now turn to DNA nanotechnology, the brainchild of Nadrian Seeman's vision of using DNA as an architectural element (Seeman, 1982~.
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Thus, any chosen Wang tile can be implemented as a DNA molecule. Appropriate design of the molecule will encourage assembly into two-dimensional sheets.
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quences given to the tiles' sticky ends could be used to program different periodic arrangements of tiles (Winfree et al., 1998a)
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DNA COMPUTING BY SELF-ASSEMBLY ~ C ~ ~ C ~ ~ C ~ ~ C Or 111 :~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~:~: ~ ,a~ . A ~~ ~~_~ ~:~ ~~ ~~ ~~ 25 nanometers of I' ~_~A^~ ~~:~ ~~ FIGURE 3 DNA double-crossover molecules can implement abstract Wang tiles, producing a two-dimensional lattice of DNA with binding interactions dictated by the DNA sticky ends.
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POTENTIAL TECHNOLOGICAL APPLICATIONS Combinatorial Optimization Problems Solving combinatorial optimization problems, in the spirit of Adleman's original paper, was the first application considered for algorithmic self-assembly. Adleman's essential insight is based on the fact that a class of hard computational problems, the NP-complete problems, share a common generate-and-test form does a sequence exist that satisfies easy-to-check properties X, Y
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autonomous biochemical algorithms. A more promising application is suggested by examining how selfassembly is used in biology.
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Any circuit pattern that has a concise algorithmic description is a potential target for this approach. Small tile sets have been designed for demultiplexers, such as the ones necessary to access a RAM memory (shown in Figure 5)
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on To : ~ ~ : .~ : .4 : i ~ l: ~ ~ : .4 : .4 : i ~ l: ~ ~ : .~ : .4 : i ~ l: ~ ~ -E l ~ ~ ~ ~- ~ ~ l - ~ ~ ~ ~ ~ ~ ~ - ~ L it,,, ,5....
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Recent theoretical work has suggested the possibility of error-correcting tile sets for self-assembly, which, if demonstrated experimentally, would significantly increase the feasibility of interesting applications. A second prevalent source of algorithmic errors is undesired nucleation (analogous to programs starting by themselves with random input)
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Caltech Computer Science Technical Report 1998.22. Pasadena: California Institute of Technology.
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Natural Computation as a Principle of Biological Design WIEEEM P
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Natural computing and evolutionary optimization can, in principle, be extended to the design of a wide variety of nonbiological objects. Developing better methods of universal (i.e., numerical)
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In this paper, I will describe the building blocks for these genetic circuits, several intracellular and intercellular prototype genetic circuits that have been implemented recently, some of the challenges in designing such circuits, and the long-term significance of this work. SYNTHETIC GENE NETWORKS Genetic circuits are collections of basic elements that interact to produce a particular behavior.
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Theoretically, any arbitrary digital logic function can be implemented with genetic circuits using these gates. By constructing biochemical logic circuits and embedding them in cells, one can extend or modify the behavior of cells.
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. COMMUNICATION AND SIGNAL PROCESSING Intercellular communication allows individual cells to coordinate their behavior and accomplish sophisticated tasks they simply cannot perform alone.
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To explore potential applications that would benefit from coordinated, multicellular behavior, we have begun to integrate communication capabilities with various synthetic genetic regulatory and information-processing networks. Toward this end, we first programmed Escherichia cold cells to communicate with each other by connecting several transcriptional regulatory elements with previously unrelated signaling elements from the marine bacterium Vibrio fischeri (Weiss and Knight, 2000~.
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An important long-term goal for this type of research is to be able to tune the responses of genetic circuits with the same predictability and reliability as we can when we design electrical devices. By combining this type of analog information processing with digital computation and programmed cell-tocell communication, we may be able to create a flexible and powerful engineering discipline for programmed cell behaviors.
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However, an important subset of this component library should be devoted to interfacing with the host to control desired cellular functions, such as the production of specific enzymes during predefined conditions, control over cell replication, and programmed secretion of various chemicals. Designing operational and efficient genetic circuits will require models and simulation tools that can provide accurate quantitative predictions of circuit behavior.
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128 FRONTIERS OF ENGINEERING FIGURE 5 Fluorescence microscope images of "sentinel" bacterial cells programmed to detect whether the concentration of a chemical secreted by another type of bacteria is above or below particular thresholds. The bacteria that secrete the biochemical are fluorescing in red, while the sentinel bacteria scattered throughout the entire viewing area are fluorescing in yellow when the prespecified detection conditions are satisfied.
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Effects on the host's environment must be understood and modeled to design complex synthetic circuits. Genetic circuits must integrate specific components or network motifs that make them robust to fluctuations in the kinetics of biochemical reactions.
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2003. Genetic circuit building blocks for cellular computation, communications, and signal processing.
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