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检索条件"任意字段=27th International Conference on DNA Computing and Molecular Programming, DNA 2021"

共 13 条记录，以下是1-10 订阅

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27th international conference on dna computing and molecular programming, dna 2021

27th International Conference on DNA Computing and Molecular...

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27th international conference on dna computing and molecular programming, dna 2021

ISBN: (纸本)9783959772051

the proceedings contain 11 papers. the topics discussed include: robust digital molecular design of binarized neural networks;computing properties of thermodynamic binding networks: an integer programming approach;self-replication via tile self-assembly;improved lower and upper bounds on the tile complexity of uniquely self-assembling a thin rectangle non-cooperatively in 3D;directed non-cooperative tile assembly is decidable;molecular machines from topological linkages;small tile sets that compute while solving mazes;reactamole: functional reactive molecular programming;parallel pairwise operations on data stored in dna: sorting, shifting, and searching;and predicting minimum free energy structures of multi-stranded nucleic acid complexes is APX-Hard.

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Parallel pairwise operations on data stored in dna: Sorting, shifting, and searching 27

Parallel pairwise operations on data stored in dna: Sorting,...

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27th international conference on dna computing and molecular programming, dna 2021

作者： Chen, Tonglin Solanki, Arnav Riedel, Marc Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN United States

ISBN: (纸本)9783959772051

Prior research has introduced the Single-Instruction-Multiple-Data paradigm for dna computing (SIMD dna). It offers the potential for storing information and performing in-memory computations on dna, with massive parallelism. this paper introduces three new SIMD dna operations: Sorting, shifting, and searching. Each is a fundamental operation in computer science. Our implementations demonstrate the effectiveness of parallel pairwise operations with this new paradigm. © 2021 Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. All rights reserved.

关键词： Sorting

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Proceedings of the 27th Hawaii international conference on System Sciences (HICSS-27). Part 5 (of 5)

Proceedings of the 27th Hawaii International Conference on S...

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the 27th Hawaii international conference on System Sciences, HICSS-27

ISBN: (纸本)0818650907

the proceedings contains 47 papers on the applications of computing to natural sciences. Topics discussed include dna sequences, proteins, biology, data structures, databases, computer systems, algorithms, molecular structures, genomes, data reduction, drugs, computer programming, computer programming languages, user interfaces, computer graphics and systems analysis.

关键词： Natural sciences computing

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computing properties of thermodynamic binding networks: An integer programming approach 27

Computing properties of thermodynamic binding networks: An i...

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27th international conference on dna computing and molecular programming, dna 2021

作者： Haley, David Doty, David University of California DavisCA United States

ISBN: (纸本)9783959772051

the thermodynamic binding networks (TBN) model [9] is a tool for studying engineered molecular systems. the TBN model allows one to reason about their behavior through a simplified abstraction that ignores details about molecular composition, focusing on two key determinants of a system's energetics common to any chemical substrate: How many molecular bonds are formed, and how many separate complexes exist in the system. We formulate as an integer program the NP-hard problem of computing stable (a.k.a., minimum energy) configurations of a TBN: those configurations that maximize the number of bonds and complexes. We provide open-source software [13] solving this integer program. We give empirical evidence that this approach enables dramatically faster computation of TBN stable configurations than previous approaches based on SAT solvers [3]. Furthermore, unlike SAT-based approaches, our integer programming formulation can reason about TBNs in which some molecules have unbounded counts. these improvements in turn allow us to efficiently automate verification of desired properties of practical TBNs. Finally, we show that the TBN has a natural representation with a unique Hilbert basis describing the "fundamental components" out of which locally minimal energy configurations are composed. this characterization helps verify correctness of not only stable configurations, but entire "kinetic pathways" in a TBN. © 2021 Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. All rights reserved.

关键词： thermodynamics

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molecular machines from topological linkages 27

Molecular machines from topological linkages

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27th international conference on dna computing and molecular programming, dna 2021

作者： Breik, Keenan Luchsinger, Austin Soloveichik, David The University of Texas AustinTX United States

ISBN: (纸本)9783959772051

Life is built upon amazingly sophisticated molecular machines whose behavior combines mechanical and chemical action. Engineering of similarly complex nanoscale devices from first principles remains an as yet unrealized goal of bioengineering. In this paper we formalize a simple model of mechanical motion (mechanical linkages) combined with chemical bonding. the model has a natural implementation using dna with double-stranded rigid links, and single-stranded flexible joints and binding sites. Surprisingly, we show that much of the complex behavior is preserved in an idealized topological model which considers solely the graph connectivity of the linkages. We show a number of artifacts including Boolean logic, catalysts, a fueled motor, and chemo-mechanical coupling, all of which can be understood and reasoned about in the topological model. the variety of achieved behaviors supports the use of topological chemical linkages in understanding and engineering complex molecular behaviors. © 2021 Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. All rights reserved.

关键词： Binding sites

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Reactamole: Functional reactive molecular programming 27

Reactamole: Functional reactive molecular programming

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27th international conference on dna computing and molecular programming, dna 2021

作者： Klinge, Titus H. Lathrop, James I. Osera, Peter-Michael Rogers, Allison Drake University Des MoinesIA United States Iowa State University AmesIA United States Grinnell College GrinnellIA United States

ISBN: (纸本)9783959772051

Chemical reaction networks (CRNs) are an important tool for molecular programming, a field that is rapidly expanding our ability to deploy computer programs into biological systems for a variety of applications. However, CRNs are also difficult to work with due to their massively parallel nature, leading to the need for higher-level languages that allow for easier computation with CRNs. Recently, research has been conducted into a variety of higher-level languages for deterministic CRNs but modeling CRN parallelism, managing error accumulation, and finding natural CRN representations are ongoing challenges. We introduce Reactamole, a higher-level language for deterministic CRNs that utilizes the functional reactive programming (FRP) paradigm to represent CRNs as a reactive dataflow network. Reactamole equates a CRN with a functional reactive program, implementing the key primitives of the FRP paradigm directly as CRNs. the functional nature of Reactamole makes reasoning about molecular programs easier, and its strong static typing allows us to ensure that a CRN is well-formed by virtue of being well-typed. In this paper, we describe the design of Reactamole and how we use CRNs to represent the common datatypes and operations found in FRP. We also demonstrate the potential of this functional reactive approach to molecular programming by giving an extended example where a CRN is constructed using FRP to modulate and demodulate an amplitude modulated signal. © 2021 Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. All rights reserved.

关键词： Application programs

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Robust digital molecular design of binarized neural networks 27

Robust digital molecular design of binarized neural networks

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27th international conference on dna computing and molecular programming, dna 2021

作者： Linder, Johannes Chen, Yuan-Jyue Wong, David Seelig, Georg Ceze, Luis Strauss, Karin University of Washington Paul G. Allen School of Computer Science and Engineering SeattleWA United States Microsoft Research RedmondWA United States University of Washington Department of Bioengineering SeattleWA United States University of Washington Department of Electrical and Computer Engineering SeattleWA United States

ISBN: (纸本)9783959772051

molecular programming - a paradigm wherein molecules are engineered to perform computation - shows great potential for applications in nanotechnology, disease diagnostics and smart therapeutics. A key challenge is to identify systematic approaches for compiling abstract models of computation to molecules. Due to their wide applicability, one of the most useful abstractions to realize is neural networks. In prior work, real-valued weights were achieved by individually controlling the concentrations of the corresponding "weight" molecules. However, large-scale preparation of reactants with precise concentrations quickly becomes intractable. Here, we propose to bypass this fundamental problem using Binarized Neural Networks (BNNs), a model that is highly scalable in a molecular setting due to the small number of distinct weight values. We devise a noise-tolerant digital molecular circuit that compactly implements a majority voting operation on binary-valued inputs to compute the neuron output. the network is also rate-independent, meaning the speed at which individual reactions occur does not affect the computation, further increasing robustness to noise. We first demonstrate our design on the MNIST classification task by simulating the system as idealized chemical reactions. Next, we map the reactions to dna strand displacement cascades, providing simulation results that demonstrate the practical feasibility of our approach. We perform extensive noise tolerance simulations, showing that digital molecular neurons are notably more robust to noise in the concentrations of chemical reactants compared to their analog counterparts. Finally, we provide initial experimental results of a single binarized neuron. Our work suggests a solid framework for building even more complex neural network computation. © 2021 Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. All rights reserved.

关键词： Neural networks

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Ensnano: A 3d modeling software for dna nanostructures 27

Ensnano: A 3d modeling software for dna nanostructures

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27th international conference on dna computing and molecular programming, dna 2021

作者： Levy, Nicolas Schabanel, Nicolas France and Ixxi France

ISBN: (纸本)9783959772051

Since the 1990s, increasingly complex nanostructures have been reliably obtained out of self-assembled dna strands: From "simple" 2D shapes to 3D gears and articulated nano-objects, and even computing structures. the success of the assembly of these structures relies on a fine tuning of their structure to match the peculiar geometry of dna helices. Various softwares have been developed to help the designer. these softwares provide essentially four kind of tools: An abstract representation of dna helices (e.g. cadnano, scadnano, dnapen, 3dna, Hex-tiles);a 3D view of the design (e.g., vHelix, Adenita, oxdnaviewer);fully automated design (e.g., BScOR, Daedalus, Perdix, Talos, Athena), generally dedicated to a specific kind of design, such as wireframe origami;and coarse grain or thermodynamical physics simulations (e.g., oxdna, Mrdna, SNUPI, Nupack, ViennaRNA,.). Magicdna combines some of these approaches to ease the design of configurable dna origamis. We present our first step in the direction of conciliating all these different approaches and purposes into one single reliable GUI solution: the first fully usable version (design from scratch to export) of our general purpose 3D dna nanostructure design software ENSnano. We believe that its intuitive, swift and yet powerful graphical interface, combining 2D and 3D editable views, allows fast and precise editing of dna nanostructures. It also handles editing of large 2D/3D structures smoothly, and imports from the most common solutions. Our software extends the concept of grids introduced in cadnano. Grids allow to abstract and articulated the different parts of a design. ENSnano also provides new design tools which speeds up considerably the design of complex large 3D structures, most notably: A 2D split view, which allows to edit intricate 3D structures which cannot easily be mapped in a 2D view, and a copy, paste & repeat functionality, which takes advantage of the grids to design swiftly large repetitive chunks of

关键词： dna

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Protein structure prediction. Introduction to session

Protein structure prediction. Introduction to session

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the 27th Hawaii international conference on System Sciences, HICSS-27

作者： Dunker, A.Keith Lathrop, Richard H. Washington State Univ Pullman WA United States

ISBN: (纸本)0818650907

the Central Dogma of molecular Biology is that information flows from dna sequence to RNA sequence to amino acid sequence which then folds to give protein its correct and 3D structure. One of the most successful approaches is homology modeling. the homology modelled structure is often accurate enough for detailed atomic binding studies and drug discovery programs. However homology modeling is dependent on correct sequence alignments. this paper tries to formulate the protein folding problem as combinatorial optimization. Application of the genetic algorithms to the ab initio prediction of the folded structure is one of the methods. Secondary structure prediction accuracy has risen due to the application of neural networks.

关键词： Natural sciences computing

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A Fault Tolerant and Deadline Constrained Sequence Alignment Application on Cloud-Based Spot GPU Instances 27th

A Fault Tolerant and Deadline Constrained Sequence Alignment...

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27th international European conference on Parallel and Distributed computing (Euro-Par)

作者： Brum, Rafaela C. Sousa, Walisson P. Melo, Alba C. M. A. Bentes, Cristiana de Castro, Maria Clicia S. Drummond, Lucia Maria de A. Fluminense Fed Univ Niteroi RJ Brazil Univ Estado Rio De Janeiro Rio De Janeiro Brazil Univ Brasilia Brasilia DF Brazil

ISBN: (纸本)9783030856656;9783030856649

Pairwise sequence alignment is an important application to identify regions of similarity that may indicate the relationship between two biological sequences. this is a computationally intensive task that usually requires parallel processing to provide realistic execution times. this work introduces a new framework for a deadline constrained application of sequence alignment, called MASA-CUDAlign, that exploits cloud computing with Spot GPU instances. Although much cheaper than On-Demand instances, Spot GPUs can be revoked at any time, so the framework is also able to restart MASA-CUDAlign from a checkpoint in a new instance when a revocation occurs. We evaluate the proposed framework considering five pairs of dna sequences and different AWS instances. Our results show that the framework reduces financial costs when compared to On-Demand GPU instances while meeting the deadlines even in scenarios with several instances revocations.

关键词： Cloud computing Spot GPU Sequence alignment

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