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 ...
癌症-细菌疗法的数学建模：基于物理信息神经网络的数值模拟与分析
International University of Rabat | Université de Lorraine, CNRS, CRAN | Université de Lorraine, CNRS, IECL | Univ Rennes, INSA, CNRS, IRMAR-UMR 6625
30秒速读
IN SHORT: 提供了一个严格的、无网格的PINN框架，用于模拟和分析细菌癌症疗法中复杂的、空间异质的相互作用。
核心创新
- Methodology First coupled PDE model integrating tumor, bacteria, oxygen, immunosuppressive cytokines, and quorum-sensing signals for bacterial cancer therapy.
- Theory Proved global well-posedness and stability of the five-species reaction-diffusion system, establishing a rigorous mathematical foundation.
- Methodology Established convergence guarantees for PINNs on this nonlinear system, with an overall error bound of 𝒪(n^{-2}ln^{4}(n)+N^{-1/2}).
主要结论
- PINN框架实现了𝒪(n^{-2}ln^{4}(n)+N^{-1/2})的可量化误差率，能够对耦合系统进行准确的无网格模拟。
- 敏感性分析表明，治疗效果高度依赖于缺氧维持（通过KH/(KH+O)建模）和细菌的氧耐受性。
- 该模型识别了三个生物学相关的稳态，并确认扩散不会引发图灵不稳定性，表明时空动力学的稳定性。
摘要: 细菌癌症疗法利用厌氧细菌靶向缺氧肿瘤区域的能力，然而肿瘤生长、细菌定植、氧气水平、免疫抑制细胞因子和细菌通讯之间的相互作用仍然缺乏量化。我们提出了一个二维组织域中五个耦合非线性反应-扩散方程的数学模型。我们证明了模型的全局适定性，并确定了其稳态以分析稳定性。此外，物理信息神经网络（PINN）无需网格和大量数据即可求解该系统。它通过结合残差稳定性和Sobolev近似误差界提供了收敛保证。这导致整体误差率为𝒪(n^{-2}ln^{4}(n)+N^{-1/2})，该误差率取决于网络宽度n和配置点数量N。我们进行了多项数值实验，包括预测肿瘤对治疗的反应。我们还对某些参数进行了敏感性分析。结果表明，长期治疗效果可能需要维持肿瘤中的缺氧区域，或者使用更能耐受氧气的细菌，这对于持久的肿瘤控制可能是必要的。