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SHF: Small :Digital Signal Processing with Biomolecular Reactions

SHF: Small :Digital Signal Processing with Biomolecular Reactions
SHF:小型:生物分子反应的数字信号处理
批准号:
1117168
负责人:
Keshab Parhi
金额:
$40.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2011
资助国家:
美国
项目状态:
已结题
起止时间:
2011-08-01 至 2014-07-31

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中文摘要
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英文摘要
Digital signal processing has been a cornerstone of the modern communications and electronics revolution, transforming application areas such as wired and wireless communications, storage, and biomedical signal processing. This project will study digital signal processing in an entirely new domain: molecular systems. In contrast to electronic systems, where signals are represented by time-varying voltage values, in molecular systems signals are represented by time-varying concentrations of different molecular types, such as proteins, RNA and DNA. This project will develop, implement, and evaluate molecular-level designs for a variety of digital signal processing operations such as filtering, equalization, and noise cancellation. These operations will be synchronized by clock signals, created through sustained chemical oscillations. Memory will be created by transferring signals between different molecular types in alternating phases of the clock. The key idea underpinning this research is that the computation should be essentially rate-independent: it should only depend on coarse categories for the rates of the chemical reactions (e.g., ?fast? vs. ?slow?). It should not matter how fast any ?fast? reaction is ? only that ?fast? reactions are fast relative to ?slow? reactions. Designs with this property can be mapped to different chemical substrates. They compute accurately in spite of variations in environmental conditions such as temperature. The impetus for this work is not computation per se; chemical systems will never be useful for number crunching. Rather the field of molecular computing aims for the design of custom, embedded biological ?sensors? and ?controllers? ? viruses and bacteria that are engineered to perform useful tasks in situ, such as cancer detection and drug therapy. As an experimental chassis, this project will map designs for digital signal processing operations to chemical reactions involving DNA strands. These designs will be evaluated with computer simulations of the chemical kinetics.Techniques for analyzing the dynamics of biological systems are well established. However, synthesizing computation with such mechanisms requires new techniques ? and an entirely new mindset. The digital circuit design community has unique expertise that can be brought to bear on the challenging design problems encountered in synthetic biology. Applications in biology, in turn, offer a wealth of interesting problems in algorithmic development. With its cross-disciplinary emphasis, this project will bring new perspectives to both fields.If successful, the proposed research will transform disciplines such as genetic engineering of drug-delivery systems. Currently, a costly, ineffective ad-hoc approach prevails. With robust and rate-independent techniques for implementing operations such as digital signal processing, much more effective systems will be developed. An important goal of the project is to communicate the impetus for interdisciplinary research to a wide audience. A new course will be developed, titled "Circuits, Computation, and Biology" offered jointly through the Electrical Engineering Department and the Biomedical Informatics and Computational Biology Program at the University of Minnesota. Building upon current recruitment efforts that have brought in female students, students from underrepresented groups will be recruited into the project.
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Collaborative Research: SHF: Small: Efficient and Scalable Privacy-Preserving Neural Network Inference based on Ciphertext-Ciphertext Fully Homomorphic Encryption
  • 批准号:
    2243053
  • 项目类别:
    Standard Grant
  • 资助金额:
    $32.5万
  • 财政年份:
    2023
  • 负责人:
    Keshab Parhi
  • 依托单位:
Collaborative Research: SHF: Medium: TensorNN: An Algorithm and Hardware Co-design Framework for On-device Deep Neural Network Learning using Low-rank Tensors
  • 批准号:
    1954749
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $40.0万
  • 财政年份:
    2020
  • 负责人:
    Keshab Parhi
  • 依托单位:
SHF: Small: Collaborative Research: LDPD-Net: A Framework for Accelerated Architectures for Low-Density Permuted-Diagonal Deep Neural Networks
  • 批准号:
    1814759
  • 项目类别:
    Standard Grant
  • 资助金额:
    $27.5万
  • 财政年份:
    2018
  • 负责人:
    Keshab Parhi
  • 依托单位:
EAGER: Low-Energy Architectures for Machine Learning
  • 批准号:
    1749494
  • 项目类别:
    Standard Grant
  • 资助金额:
    $12.5万
  • 财政年份:
    2017
  • 负责人:
    Keshab Parhi
  • 依托单位:
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昼夜节律性small RNA在血斑形成时间推断中的法医学应用研究
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  • 项目类别:
    省市级项目
  • 资助金额:
    --
  • 批准年份:
    2024
  • 负责人:
  • 依托单位:
tRNA-derived small RNA上调YBX1/CCL5通路参与硼替佐米诱导慢性疼痛的机制研究
  • 批准号:
  • 项目类别:
    省市级项目
  • 资助金额:
    10.0万元
  • 批准年份:
    2022
  • 负责人:
    张祥忠
  • 依托单位:
Small RNA调控I-F型CRISPR-Cas适应性免疫性的应答及分子机制
Small RNAs调控解淀粉芽胞杆菌FZB42生防功能的机制研究
  • 批准号:
    31972324
  • 项目类别:
    面上项目
  • 资助金额:
    58.0万元
  • 批准年份:
    2019
  • 负责人:
    高学文
  • 依托单位: