Paper List
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SpikGPT: A High-Accuracy and Interpretable Spiking Attention Framework for Single-Cell Annotation
This paper addresses the core challenge of robust single-cell annotation across heterogeneous datasets with batch effects and the critical need to ide...
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Unlocking hidden biomolecular conformational landscapes in diffusion models at inference time
This paper addresses the core challenge of efficiently and accurately sampling the conformational landscape of biomolecules from diffusion-based struc...
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Personalized optimization of pediatric HD-tDCS for dose consistency and target engagement
This paper addresses the critical limitation of one-size-fits-all HD-tDCS protocols in pediatric populations by developing a personalized optimization...
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Realistic Transition Paths for Large Biomolecular Systems: A Langevin Bridge Approach
This paper addresses the core challenge of generating physically realistic and computationally efficient transition paths between distinct protein con...
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Consistent Synthetic Sequences Unlock Structural Diversity in Fully Atomistic De Novo Protein Design
This paper addresses the core pain point of low sequence-structure alignment in existing synthetic datasets (e.g., AFDB), which severely limits the pe...
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MoRSAIK: Sequence Motif Reactor Simulation, Analysis and Inference Kit in Python
This work addresses the computational bottleneck in simulating prebiotic RNA reactor dynamics by developing a Python package that tracks sequence moti...
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On the Approximation of Phylogenetic Distance Functions by Artificial Neural Networks
This paper addresses the core challenge of developing computationally efficient and scalable neural network architectures that can learn accurate phyl...
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EcoCast: A Spatio-Temporal Model for Continual Biodiversity and Climate Risk Forecasting
This paper addresses the critical bottleneck in conservation: the lack of timely, high-resolution, near-term forecasts of species distribution shifts ...
Scalable DNA Ternary Full Adder Enabled by a Competitive Blocking Circuit
Institute of Computational Science and Technology, Guangzhou University, China | School of Computer Science and Technology, Wuhan University of Science and Technology, China | School of Computer Science and Technology, Dalian University of Technology, China | School of Computing Science, Peking University, China
30秒速读
IN SHORT: This paper addresses the core bottleneck of carry information attenuation and limited computational scale in DNA binary adders by introducing a scalable ternary architecture.
核心创新
- Methodology Proposes a novel Competitive Blocking (CB) circuit that leverages differential reaction kinetics (k2 ≫ k1, k3) to dynamically select and block reaction pathways for precise carry information management.
- Methodology Introduces a ternary (base-3) adder architecture, moving beyond binary systems, which inherently reduces the frequency of carry propagation and increases single-bit information density.
- Methodology Implements a Dynamic Concentration Adjustment (CA) strategy, applying chemical equilibrium principles to optimize reactant ratios and signal transmission, enabling significant bit-width extension.
主要结论
- The CB circuit reliably performs ternary full-adder logic, with experimental validation showing successful 10-bit addition operations.
- The integrated CA strategy enables the adder to scale to 17-bit addition, representing a massive increase in computational scale.
- The architecture achieves a 'scale/strand' metric improvement of 2,405,552x compared to a recent state-of-the-art binary DNA adder capable of only 4 consecutive carries.
摘要: DNA adder circuits are programmable reaction networks that process DNA molecular inputs to compute a sum and serve as essential components for digital computation. Currently, DNA adders primarily focus on binary addition. While efforts extend the operational bit-width by minimizing the number of DNA strands and developing carry-transmission mechanisms, challenges such as the susceptibility of carrying information to attenuation and the limited expressive capacity of the binary system impose significant constraints on computational scale. This paper proposes a scalable ternary adder architecture by introducing an innovative competitive blocking (CB) circuit. The architecture employs a dual cooperative optimization strategy that significantly enhances single-bit computational capacity and incorporates a dynamic concentration adjustment (CA) to effectively broaden the computational bit-width. Consequently, a significant increase in molecular computing scale is achieved compared to previous binary adders. Biochemical experimental results indicate that the CB circuit effectively outputs the ternary full-adder bit and successfully performs 10-bit addition. Furthermore, by implementing the CA strategy, this adder can be further extended to support 17-bit addition. This research provides a novel methodological foundation for advancing DNA computing technologies and offers promising potential for scalable digital computing applications.