IEEE Transactions on Molecular, Biological, and Multi-Scale Communications最新文献_第2页

Charge Transport in a Protein-Ligand Complex: A Preliminary Quantum Statistical Model 蛋白质-配体复合物中的电荷传输：一个初步的量子统计模型

IF 2.3

IEEE Transactions on Molecular, Biological, and Multi-Scale Communications Pub Date : 2026-01-02 DOI: 10.1109/TMBMC.2025.3650536

Roisin Braddell;Jone Uria-Albizuri;Jean Bernard Bru;Serafim Rodrigues

引用次数: 0

Information Transmission Through Noisy Chemical Reactions 通过嘈杂的化学反应传递信息

IF 2.3

IEEE Transactions on Molecular, Biological, and Multi-Scale Communications Pub Date : 2026-01-01 Epub Date: 2026-04-14 DOI: 10.1109/TMBMC.2026.3683972

Jingxiang Yu;Hui Li

引用次数: 0

Propelling DNA-Based Archival Storage With an Algorithmic Mindset 用算法思维推动基于dna的档案存储

IF 2.3

IEEE Transactions on Molecular, Biological, and Multi-Scale Communications Pub Date : 2026-01-01 Epub Date: 2026-03-18 DOI: 10.1109/TMBMC.2026.3694522

Yaoyu Yang;Wei Wei;Minghao Mou

引用次数: 0

Bio-Inspired Navigation for Intelligent Nanoscale Drug Delivery Systems Based on Chemotaxis and Entropy 基于趋化性和熵的智能纳米给药系统仿生导航

IF 2.5

IEEE Transactions on Molecular, Biological, and Multi-Scale Communications Pub Date : 2026-01-01 Epub Date: 2026-07-02 DOI: 10.1109/TMBMC.2026.3708193

Bahram Hedayati;Karthik Reddy Gorla;Maurizio Magarini

{"title":"Bio-Inspired Navigation for Intelligent Nanoscale Drug Delivery Systems Based on Chemotaxis and Entropy","authors":"Bahram Hedayati;Karthik Reddy Gorla;Maurizio Magarini","doi":"10.1109/TMBMC.2026.3708193","DOIUrl":"https://doi.org/10.1109/TMBMC.2026.3708193","url":null,"abstract":"Biological intelligence provides a powerful paradigm for developing novel computational and communication frameworks inspired by living systems. Addressing complex diseases such as cancer, which remains one of the most persistent global health challenges, requires intelligent and targeted therapeutic strategies capable of distinguishing and eliminating malignant cells while preserving surrounding healthy tissue. In this work, we propose a biologically inspired navigation framework that combines chemotaxis, a natural process that governs directional cell movement in response to chemical gradients, with entropy exploration to guide nanoscale medical agents (NMA) toward cancerous regions within the tumor microenvironment (TME). The hybrid chemotaxis–entropy mechanism emulates adaptive decision making observed in biological organisms, enabling autonomous localization and tumor treatment through targeted drug delivery. Using hypoxia as a biomarker, due to the elevated oxygen consumption of cancerous cells, numerical simulations are performed for both single- and multi-tumor configurations. Comparative analyses in multiple performance metrics reveal that the hybrid navigation approach achieves higher efficiency, reliability, and tumor elimination rates compared to random and pure chemotaxis strategies. Furthermore, the feasibility of biologically grounded navigation is demonstrated to be significantly enhanced through an entropy-guided perspective, highlighting its potential for intelligent control and communication in nanoscale systems. This study establishes a computational framework linking biological intelligence, molecular communication, and nanoscale drug delivery, laying the foundation for future bio-inspired optimization of drug localization in cancer therapy. Testing","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"12 ","pages":"705-715"},"PeriodicalIF":2.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11593977","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148506908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Guest Editorial Interconnecting Molecular and Terahertz Communications for Future 6G/7G Networks 未来6G/7G网络互连分子和太赫兹通信

IF 2.5

IEEE Transactions on Molecular, Biological, and Multi-Scale Communications Pub Date : 2026-01-01 Epub Date: 2026-02-20 DOI: 10.1109/TMBMC.2026.3660594

Miaowen Wen;Yu Huang;Xuan Chen;Nan Yang;Yao Wei;Hadeel Elayan;Tianqi Mao

引用次数: 0

Spatial-Aware Adaptive Graph Coloring for Interference Management in Linear High-Order Modulation-Based MIMO Molecular Communication 基于线性高阶调制的MIMO分子通信干扰管理的空间感知自适应图着色

IF 2.5

IEEE Transactions on Molecular, Biological, and Multi-Scale Communications Pub Date : 2026-01-01 Epub Date: 2026-08-05 DOI: 10.1109/TMBMC.2026.3720574

Guodong Yue;Wenbo Yang;Qiang Liu;Kun Yang

{"title":"Spatial-Aware Adaptive Graph Coloring for Interference Management in Linear High-Order Modulation-Based MIMO Molecular Communication","authors":"Guodong Yue;Wenbo Yang;Qiang Liu;Kun Yang","doi":"10.1109/TMBMC.2026.3720574","DOIUrl":"https://doi.org/10.1109/TMBMC.2026.3720574","url":null,"abstract":"Diffusive molecular communication (MC) with multiple-input multiple-output (MIMO) architectures is a promising approach for improving the throughput of nanonetworks. However, high-order concentration modulation in dense MIMO systems is highly vulnerable to inter-link interference (ILI), while fully orthogonal resource allocation requires a large number of distinguishable molecule types. To address this problem, this paper proposes a joint modulation and interference-management framework that combines linear high-order concentration modulation (LHOCM) with spatial-aware adaptive graph coloring (SA-AGC). At the link level, LHOCM increases the number of bits conveyed per symbol, and Gaussian-intersection thresholds are used to account for signal-dependent noise in molecular reception. At the network level, SA-AGC converts reliability-constrained interference relationships into a conflict graph and assigns molecule types by separating strong interfering links while allowing spatial reuse among weakly coupled links. Numerical results show that the proposed framework substantially suppresses the interference-induced error floor and improves the achievable throughput under a limited molecule-type budget, compared with full-reuse and TDMA baselines.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"12 ","pages":"900-913"},"PeriodicalIF":2.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148780957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Open-Quantum-Systems Theory of Quantum-Biological Communication Channels 量子生物通信信道的开放量子系统理论

IF 2.5

IEEE Transactions on Molecular, Biological, and Multi-Scale Communications Pub Date : 2026-01-01 Epub Date: 2026-07-23 DOI: 10.1109/TMBMC.2026.3716243

Liang Dong

{"title":"An Open-Quantum-Systems Theory of Quantum-Biological Communication Channels","authors":"Liang Dong","doi":"10.1109/TMBMC.2026.3716243","DOIUrl":"https://doi.org/10.1109/TMBMC.2026.3716243","url":null,"abstract":"Quantum biology and communication engineering remain only partly integrated. One is organized around mechanisms, the other around input–output channels. This paper joins them. We model a <italic>quantum-biological communication channel</i> (QBCC) as an open quantum system: an input is encoded into a biological quantum subsystem, the state evolves under Gorini–Kossakowski–Sudarshan–Lindblad dynamics in a structured bath, and a classical readout induces a channel law <inline-formula> <tex-math>$W_{T}(y|x)$ </tex-math></inline-formula>. When is such a system a channel at all, rather than an observed quantum process? An operational criterion answers this, built on an arbitrary-but-fixed encoding and a commuting abstraction–representation diagram. The channel law then connects to mutual information, capacity, quantum Fisher information, and a noise-assistance index. Four reusable primitives follow: radical-pair receivers, exciton routers, proton-tunneling genetic error channels, and ion-coherence links. One illustrative network and four literature-informed case studies instantiate them. A penalized likelihood-ratio test is then applied to two of them. It separates a phase-sensitive interference channel, not reproduced by the sign-blind rate null, from the Fenna–Matthews–Olson complex, whose noise-assisted transport that null already reproduces. A falsifiability workflow, two worked applications, and a five-layer network stack complete the framework.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"12 ","pages":"858-874"},"PeriodicalIF":2.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148780945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spiking Neural Networks for Efficient Classification of Peripheral Nerve Stimuli 脉冲神经网络对周围神经刺激的有效分类

IF 2.5

IEEE Transactions on Molecular, Biological, and Multi-Scale Communications Pub Date : 2026-01-01 Epub Date: 2026-09-03 DOI: 10.1109/TMBMC.2026.3729935

Silvia Mura;Arek Berç Gökdağ;Antonio Coviello;Umberto Spagnolini;Maurizio Magarini

{"title":"Spiking Neural Networks for Efficient Classification of Peripheral Nerve Stimuli","authors":"Silvia Mura;Arek Berç Gökdağ;Antonio Coviello;Umberto Spagnolini;Maurizio Magarini","doi":"10.1109/TMBMC.2026.3729935","DOIUrl":"https://doi.org/10.1109/TMBMC.2026.3729935","url":null,"abstract":"Accurate electroneurographic (ENG) signal classification is a key function in biological and neural communication systems, where peripheral nerves act as noisy, bandwidth-limited information channels interfacing with implantable bioelectronic devices. In neural decoding and stimulation (ND&S) systems, ENG signals are transmitted and subsequently classified to extract task-relevant information prior to stimulation. This processing must operate in real-time on resource-constrained implantable platforms, limiting the applicability of conventional deep neural networks in molecular and biological communication settings. To address these challenges, we propose two event-driven spiking neural network (SNN) architectures for energy-efficient ENG decoding. A Parametric Leaky Integrate-and-Fire Spiking Neural Network (PLIF-SNN) employs trainable membrane time constants to model temporal dynamics, while a Parallel Spiking Neural Network (PSNN) uses parallel spiking neurons with learnable temporal kernels and reset-free dynamics for compact temporal filtering. The proposed models were evaluated on a multichannel cuff-electrode dataset recorded from nine Long Evans rats and compared with ESCAPE-NET and MobilESCAPE-NET. For single compound action potential classification, PSNN achieved 87.21% accuracy and an 84.98% macro F1-score using only 25.5k parameters, reducing model complexity by up to 99%. On 500-sample windows, PLIF-SNN reached 90.25% accuracy with 15.4k parameters, matching MobilESCAPE-NET performance with one quarter of its parameter count. These results demonstrate that event-driven SNN-based decoding enables state-of-the-art ENG information extraction with substantially reduced computational and energy costs, supporting low-power, low-latency bioelectronic platforms for bidirectional biological and multiscale communication systems.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"12 ","pages":"982-994"},"PeriodicalIF":2.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149033610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Multi-Layer Deep Graph Attention Model for Decoding lncRNA–miRNA–mRNA Communication Networks 解码lncRNA-miRNA-mRNA通信网络的多层深度图注意模型

IF 2.5

IEEE Transactions on Molecular, Biological, and Multi-Scale Communications Pub Date : 2026-01-01 Epub Date: 2026-08-05 DOI: 10.1109/TMBMC.2026.3720579

Shivani Saxena;Ahsan Z. Rizvi

{"title":"A Multi-Layer Deep Graph Attention Model for Decoding lncRNA–miRNA–mRNA Communication Networks","authors":"Shivani Saxena;Ahsan Z. Rizvi","doi":"10.1109/TMBMC.2026.3720579","DOIUrl":"https://doi.org/10.1109/TMBMC.2026.3720579","url":null,"abstract":"Dysregulation of lncRNA–miRNA–mRNA (LncM2) communication networks, a key mechanism of competing endogenous RNA (ceRNA) crosstalk, plays a critical role in cancer progression. Current computational approaches are limited by their dependence on predicted interactions, lack of interpretability, and inability to effectively address transcriptomic heterogeneity. To overcome these challenges, we present MDGAM-LncM2Net, a Multi-layer Deep Graph Attention Model for reconstructing directed, context-specific LncM2 communication networks from multi-omics data. Our framework combines experimentally validated RNA interactions with transcriptomic profiles through a comprehensive analytical pipeline incorporating differential expression analysis, hypergeometric testing, Spearman correlation, and Fast Causal Inference (FCI) for directional relationship inference. MDGAM-LncM2Net leverages Graph Attention Networks (GATs) to capture topological features and Bidirectional LSTMs to model temporal expression patterns, enabling interpretable survival prediction. When applied to four cancer types (LSCC, OvCa, PrCa, and GBM), the model identified 14 pan-cancer driver lncRNAs and 11 conserved prognostic gene clusters with cancer-specific communication nodes strongly associated with patient survival. The proposed framework achieves an F1-score of 90.29% and AUC of 90.60%, representing an average improvement over the state-of-art methods. MDGAM-LncM2Net provides a robust platform for elucidating LncM2 network, with significant potential for advancing precision oncology through biomarker discovery and targeted therapy development.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"12 ","pages":"1092-1099"},"PeriodicalIF":2.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149130787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ChemSICal-Net: Timing-Controlled Chemical Reaction Network for Successive Interference Cancellation in Molecular Multiple Access 化学网络：用于分子多址连续干扰消除的定时控制化学反应网络

IF 2.5

IEEE Transactions on Molecular, Biological, and Multi-Scale Communications Pub Date : 2026-01-01 Epub Date: 2026-09-07 DOI: 10.1109/TMBMC.2026.3731403

Alexander Wietfeld;Oğuz Turgut;Eneritz Somoza Rodríguez;Wolfgang Kellerer

{"title":"ChemSICal-Net: Timing-Controlled Chemical Reaction Network for Successive Interference Cancellation in Molecular Multiple Access","authors":"Alexander Wietfeld;Oğuz Turgut;Eneritz Somoza Rodríguez;Wolfgang Kellerer","doi":"10.1109/TMBMC.2026.3731403","DOIUrl":"https://doi.org/10.1109/TMBMC.2026.3731403","url":null,"abstract":"Molecular communication (MC) networks between nanoscale biological entities require biologically plausible implementations of signal processing algorithms. Chemical reaction networks (CRNs) provide a natural framework to model such computing processes and capture stochastic effects in simulations. In this work, we present ChemSICal-Net, a stochastic CRN receiver model that implements successive interference cancellation (SIC) for molecular multiple access. The receiver maps SIC to modular chemical building blocks and uses a chemical oscillator for timing control. We further propose an adaptive Bayesian optimization (BO) scheme for tuning reaction rate constants and initial molecule counts, and compare it with baseline optimizers under a fair simulation-cost metric. Then, we evaluate ChemSICal-Net stochastically across clock speeds and configurations using communication metrics such as detection accuracy and decision time. The results show that chemical timing reduces error probability by up to a factor of 2 for short decision times, revealing a direct decision-time/error trade-off. BO reduces the error probability by approximately one order of magnitude compared with non-optimized parameters. Overall, the results support a multi-scale design methodology that combines external optimization with stochastic reaction-level simulation for communication-oriented, timing-controlled CRN receivers in self-organizing living systems.","PeriodicalId":36530,"journal":{"name":"IEEE Transactions on Molecular, Biological, and Multi-Scale Communications","volume":"12 ","pages":"1037-1055"},"PeriodicalIF":2.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11683287","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149130764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0